Control method, device and readable storage medium of accessories management system

Abstract

The present invention relates to the technical field of after-sales systems, and in particular to a control method, device and readable storage medium for a parts management system. The method receives multi-source heterogeneous data related to equipment after-sales transmitted by a parts data interface, and generates an equipment parts database based on the multi-source heterogeneous data; generates a parts demand linkage prediction model based on the association relationship between each key part in the equipment parts database; after entering the parts prediction process, inputs the received region ID and equipment ID into the parts demand linkage prediction model, and generates a short-term parts prediction result corresponding to the region ID and equipment ID through the parts demand linkage prediction model. The method realizes the precision and dynamic nature of parts demand prediction, thereby improving the accuracy of parts reserves and the timeliness of parts supply.

Description

Technical Field

[0001] The present invention relates to the technical field of after-sales systems, and in particular to a control method, a device and a readable storage medium for a spare parts management system. Background Art

[0002] In order to ensure the timeliness of after-sales service response, it is necessary to reasonably reserve equipment accessories. However, due to the wide variety of accessories, the demand for accessories varies in different regions, different equipment, and different models. Therefore, it is difficult to accurately grasp the reserve quantity of various accessories by relying on traditional manual experience to predict. If the reserve is insufficient, the timeliness of after-sales service response will be affected; if the reserve is excessive, it will occupy funds and increase reserve costs.

[0003] The relevant prediction technology takes into account the two factors of historical consumption and failure prediction, but ignores other important factors that affect the demand for spare parts, such as the total number of equipment currently in use in each region, the consumption rate of spare parts of equipment at different stages of use, the number of equipment of different models in use, the service life of each model of equipment, and the loss of spare parts during storage and maintenance. Since it only relies on historical data extrapolation and there is no linkage between products and accessories, accessories and accessories, etc., the relevant equipment accessories reserve prediction method has the defects of insufficient prediction accuracy and dynamics.

[0004] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of the present invention is to provide a control method for a spare parts management system, aiming to solve the problem that the reserve prediction method of related equipment spare parts has insufficient prediction accuracy and dynamics.

[0006] To achieve the above object, the present invention provides a control method for a spare parts management system, the control method for the spare parts management system comprising the following steps:

[0007] Receiving multi-source heterogeneous data related to equipment after-sales service transmitted by the accessories data interface, and generating an equipment accessories database according to the multi-source heterogeneous data;

[0008] Generate a linkage prediction model for accessory demand based on the association relationship between key accessories in the equipment accessories database;

[0009] After entering the accessories prediction process, the received region identifier and device identifier are input into the accessories demand linkage prediction model, and the accessories demand linkage prediction model is used to generate a short-term accessories prediction result corresponding to the region identifier and device identifier.

[0010] Optionally, after the step of generating the short-term spare parts prediction result corresponding to the region identifier and the equipment identifier by using the spare parts demand linkage prediction model, the step further includes:

[0011] Inputting the short-term spare parts forecast result into the spare parts demand linkage forecast model;

[0012] After the accessory demand linkage prediction model receives the short-term accessory prediction result, it outputs a mid- to long-term accessory prediction result based on the short-term accessory prediction result.

[0013] Optionally, before the step of receiving multi-source heterogeneous data related to equipment after-sales transmitted by the accessory data interface and generating an equipment accessory database according to the multi-source heterogeneous data, the step further includes:

[0014] Establish a connection between the parts data interface and the parts cloud warehouse platform, enterprise resource system and customer management system;

[0015] When the data related to the after-sales service of the equipment in the accessories cloud warehouse platform, the enterprise resource system and the customer management system are updated, the data related to the after-sales service of the equipment are received based on the accessories data interface.

[0016] Optionally, the step of receiving multi-source heterogeneous data related to equipment after-sales transmitted by the accessory data interface and generating an equipment accessory database according to the multi-source heterogeneous data includes:

[0017] After receiving the multi-source heterogeneous data, attaching data identifiers to the multi-source heterogeneous data according to sources of the multi-source heterogeneous data to generate accessory data, wherein the data identifiers include product attribute identifiers, accessory relationship identifiers, and maintenance time identifiers;

[0018] The device accessories database is generated based on the data identifiers of the respective accessories data.

[0019] Optionally, before the step of generating the accessory demand linkage prediction model according to the association relationship between each key accessory in the equipment accessory database, the step further includes:

[0020] Calling a preset association rule algorithm to perform an association relationship analysis operation on the accessory data in the device accessory database;

[0021] According to the association analysis operation, determining the association corresponding to each of the accessory data, the association including the association between the product and the accessory, and the association between the accessory and the accessory;

[0022] According to different accessory data, a preset confidence threshold corresponding to the accessory data is called, and the association relationship of the key accessories is determined in the association relationship corresponding to each accessory data.

[0023] Optionally, after the step of calling a preset confidence threshold corresponding to the accessory data according to different accessory data and determining the association relationship of the key accessory in the association relationship corresponding to each accessory data, the step further includes:

[0024] When the device accessory database is updated, an update action of the association relationship of key accessories is executed.

[0025] Optionally, the step of inputting the received region identifier and device identifier into the parts demand linkage prediction model, and generating a short-term parts prediction result corresponding to the region identifier and device identifier by using the parts demand linkage prediction model includes:

[0026] After receiving the region identifier and the device identifier, the parts demand linkage prediction model reads historical sales data, warranty repair data, and parts consumption data of the target device in the target region from the device parts database based on the region identifier and the device identifier;

[0027] According to the historical sales data, warranty repair data and spare parts consumption data, demand forecasting is performed on the spare parts of the target device to obtain the demand quantity of each spare part of the target device;

[0028] The demand quantity of each accessory of the target device is output as the short-term accessory prediction result.

[0029] Optionally, after the step of generating the accessory demand linkage prediction model according to the association relationship between each key accessory in the equipment accessory database, the step further includes:

[0030] At every preset time period, the historical forecast data within the preset time period is output based on the model verification interface;

[0031] According to the model adjustment parameters received by the model verification interface, the model parameters of the accessory demand linkage prediction model are adjusted.

[0032] In addition, to achieve the above-mentioned purpose, the present invention also provides a control device for an accessories management system, which includes a memory, a processor, and a control program for the accessories management system stored in the memory and executable on the processor. When the control program of the accessories management system is executed by the processor, the steps of the control method for the accessories management system as described above are implemented.

[0033] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which a control program of the accessories management system is stored. When the control program of the accessories management system is executed by a processor, the steps of the control method of the accessories management system as described above are implemented.

[0034] The embodiment of the present invention provides a control method, device and readable storage medium of a spare parts management system. By receiving multi-source heterogeneous data transmitted by a spare parts data interface, data related to the after-sales service of the equipment from different sources can be obtained. In this way, spare parts information from different channels can be comprehensively collected and integrated; by integrating multi-source heterogeneous data and generating a device spare parts database with the association relationship of each key spare part, a data basis is provided for realizing the precision of spare parts demand prediction. By generating a spare parts demand linkage prediction model according to the association relationship of each key spare part in the device spare parts database, the mutual influence and demand association between spare parts and between equipment and spare parts can be more accurately predicted, thereby realizing the dynamic forecast of spare parts demand. By inputting the region identifier and the device identifier into the spare parts demand linkage prediction model, the prediction model can make timely spare parts demand prediction according to the latest data and association relationship, generate short-term spare parts prediction results corresponding to the region and the equipment, thereby improving the accuracy of spare parts reserve and the timeliness of supply. Therefore, the control method of the spare parts management system realizes the precision and dynamic forecast of spare parts demand through the use of multi-source heterogeneous data, the association relationship modeling of key spare parts and the generation of short-term spare parts prediction results, thereby improving the accuracy of spare parts reserve and the timeliness of spare parts supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present invention, and together with the specification are used to explain the principles of the present invention. In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the description of the embodiments are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor.

[0036] Figure 1 A schematic diagram of the architecture of the hardware operating environment of the control device of the accessories management system involved in the embodiment of the present invention;

[0037] Figure 2 A schematic flow chart of a first embodiment of a control method for a spare parts management system according to the present invention;

[0038] Figure 3 A schematic flow chart of a second embodiment of a control method for a spare parts management system according to the present invention;

[0039] Figure 4 FIG. 4 is a flow chart of a third embodiment of a control method of a spare parts management system according to the present invention.

[0040] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0041] The control method of the spare parts management system of the present application receives multi-source heterogeneous data related to equipment after-sales transmitted by the spare parts data interface, and generates an equipment spare parts database according to the multi-source heterogeneous data; generates a spare parts demand linkage prediction model according to the association relationship of each key spare part in the equipment spare parts database; after entering the spare parts prediction process, inputs the received region identification and equipment identification into the spare parts demand linkage prediction model, and generates a short-term spare parts prediction result corresponding to the region identification and equipment identification through the spare parts demand linkage prediction model. The spare parts demand prediction is made more precise and dynamic, thereby improving the accuracy of spare parts reserves and the timeliness of spare parts supply.

[0042] In order to better understand the above technical solution, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0043] As an implementation plan, Figure 1 It is a schematic diagram of the architecture of the hardware operating environment of the control device of the accessory management system involved in the embodiment of the present invention.

[0044] like Figure 1 As shown, the control device of the accessory management system may include: a processor 101, such as a core processor, a memory 102, and a communication bus 103. Among them, the memory 102 can be a high-speed random access memory (Random Access Memory, RAM) memory, or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. The memory 102 can also be a storage device independent of the aforementioned processor 101. The communication bus 103 is used to realize the connection and communication between these components.

[0045] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the control device of the accessory management system, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0046] like Figure 1 As shown, the memory 102 as a computer-readable storage medium may include an operating system, a data storage module, a network communication module, a user interface module, and a control program of the accessory management system.

[0047] exist Figure 1In the control device of the accessory management system shown, the processor 101 and the memory 102 may be arranged in the control device of the accessory management system. The control device of the accessory management system calls the control program of the accessory management system stored in the memory 102 through the processor 101, and performs the following operations:

[0048] Receiving multi-source heterogeneous data related to equipment after-sales service transmitted by the accessories data interface, and generating an equipment accessories database according to the multi-source heterogeneous data;

[0049] Generate a linkage prediction model for accessory demand based on the association relationship between key accessories in the equipment accessories database;

[0050] After entering the accessories prediction process, the received region identifier and device identifier are input into the accessories demand linkage prediction model, and the accessories demand linkage prediction model is used to generate a short-term accessories prediction result corresponding to the region identifier and device identifier.

[0051] In one embodiment, the processor 101 may be used to call a control program of the accessory management system stored in the memory 102 and perform the following operations:

[0052] Inputting the short-term spare parts forecast result into the spare parts demand linkage forecast model;

[0053] After the accessory demand linkage prediction model receives the short-term accessory prediction result, it outputs a mid- to long-term accessory prediction result based on the short-term accessory prediction result.

[0054] In one embodiment, the processor 101 may be used to call a control program of the accessory management system stored in the memory 102 and perform the following operations:

[0055] Establish a connection between the parts data interface and the parts cloud warehouse platform, enterprise resource system and customer management system;

[0056] When the data related to the after-sales service of the equipment in the accessories cloud warehouse platform, the enterprise resource system and the customer management system are updated, the data related to the after-sales service of the equipment are received based on the accessories data interface.

[0057] In one embodiment, the processor 101 may be used to call a control program of the accessory management system stored in the memory 102 and perform the following operations:

[0058] After receiving the multi-source heterogeneous data, attaching data identifiers to the multi-source heterogeneous data according to sources of the multi-source heterogeneous data to generate accessory data, wherein the data identifiers include product attribute identifiers, accessory relationship identifiers, and maintenance time identifiers;

[0059] The device accessories database is generated based on the data identifiers of the respective accessories data.

[0060] In one embodiment, the processor 101 may be used to call a control program of the accessory management system stored in the memory 102 and perform the following operations:

[0061] Calling a preset association rule algorithm to perform an association relationship analysis operation on the accessory data in the device accessory database;

[0062] According to the association analysis operation, determining the association corresponding to each of the accessory data, the association including the association between the product and the accessory, and the association between the accessory and the accessory;

[0063] According to different accessory data, a preset confidence threshold corresponding to the accessory data is called, and the association relationship of the key accessories is determined in the association relationship corresponding to each accessory data.

[0064] In one embodiment, the processor 101 may be used to call a control program of the accessory management system stored in the memory 102 and perform the following operations:

[0065] When the device accessory database is updated, an update action of the association relationship of key accessories is executed.

[0066] In one embodiment, the processor 101 may be used to call a control program of the accessory management system stored in the memory 102 and perform the following operations:

[0067] After receiving the region identifier and the device identifier, the parts demand linkage prediction model reads historical sales data, warranty repair data, and parts consumption data of the target device in the target region from the device parts database based on the region identifier and the device identifier;

[0068] According to the historical sales data, warranty repair data and spare parts consumption data, demand forecasting is performed on the spare parts of the target device to obtain the demand quantity of each spare part of the target device;

[0069] The demand quantity of each accessory of the target device is output as the short-term accessory prediction result.

[0070] In one embodiment, the processor 101 may be used to call a control program of the accessory management system stored in the memory 102 and perform the following operations:

[0071] At every preset time period, the historical forecast data within the preset time period is output based on the model verification interface;

[0072] According to the model adjustment parameters received by the model verification interface, the model parameters of the accessory demand linkage prediction model are adjusted.

[0073] Based on the hardware architecture of the control device of the above-mentioned spare parts management system, an embodiment of a control method of the spare parts management system of the present invention is proposed.

[0074] Reference Figure 2 In a first embodiment, the control method of the accessory management system includes the following steps:

[0075] Step S100: receiving multi-source heterogeneous data related to equipment after-sales service transmitted by the accessories data interface, and generating an equipment accessories database according to the multi-source heterogeneous data.

[0076] In this embodiment, the accessories management system receives multi-source heterogeneous data related to equipment after-sales service sent by various data sources through the accessories data interface, and after receiving the multi-source heterogeneous data related to equipment after-sales service, first performs data processing operations such as data cleaning and data integration on the multi-source heterogeneous data to obtain processed multi-source heterogeneous data related to equipment after-sales service. Then, the equipment accessories database is generated based on the processed multi-source heterogeneous data.

[0077] Optionally, the data sources here include but are not limited to the spare parts cloud warehouse platform, enterprise resource system, and customer management system, etc. Multi-source heterogeneous data related to equipment after-sales service include but are not limited to equipment usage information, historical spare parts storage and consumption data, warranty maintenance records, equipment detection data, etc.

[0078] As an optional implementation, after receiving the multi-source heterogeneous data, the parts management system attaches data identifiers to the multi-source heterogeneous data according to the sources of the multi-source heterogeneous data to generate parts data, wherein the data identifiers include product attribute identifiers, parts relationship identifiers, and maintenance time identifiers. Then, based on the data identifiers of each of the parts data, the equipment parts database is generated.

[0079] By attaching data identifiers to multi-source heterogeneous data, we can generate accessory data related to equipment after-sales service, so that we can integrate the relevant information of equipment accessories from different data sources, making the accessory data more comprehensive. Data identifiers provide a basis for classifying and associating accessory data. Based on the identifiers of each accessory data, we can organize and manage data according to product attributes, accessory relationships and maintenance time, and build an equipment accessory database, so as to better support subsequent accessory demand forecasting and accessory supply management.

[0080] Furthermore, before receiving the multi-source heterogeneous data related to the after-sales of the equipment transmitted by the accessories data interface, the accessories management system needs to first establish a connection with the accessories cloud warehouse platform, the enterprise resource system and the customer management system; and then when the data related to the after-sales of the equipment in the accessories cloud warehouse platform, the enterprise resource system and the customer management system are updated, the data related to the after-sales of the equipment is received based on the accessories data interface.

[0081] By establishing a connection for the accessories data interface, the data related to the equipment after-sales service can be updated in real time to the equipment accessories database. When the data in the accessories cloud warehouse platform, enterprise resource system or customer management system changes, the interface can transmit the updated data in time to ensure the accuracy and real-time performance of the equipment accessories database. By receiving data from the accessories cloud warehouse platform, enterprise resource system and customer management system, the data related to the equipment after-sales service in different systems and platforms can be obtained, which improves the comprehensiveness of the accessories database and provides a more comprehensive data basis for subsequent accessories prediction. By establishing a connection for the accessories data interface, the automatic collection and transmission of data can be realized, avoiding the tedious process of manual collection and input of data, saving time and labor costs, and reducing the error rate of data collection and import, and improving the accuracy of data. Therefore, by establishing a connection for the accessories data interface with the accessories cloud warehouse platform, enterprise resource system and customer management system, and receiving data updates related to the equipment after-sales service based on the interface, real-time synchronization of data, diversification of data sources and automatic data collection can be realized, thereby providing a more reliable, comprehensive and efficient data basis for generating an equipment accessories database.

[0082] Step S200: generating an accessory demand linkage prediction model according to the association relationship between each key accessory in the equipment accessory database.

[0083] In this embodiment, after the device parts database is generated, a parts demand linkage prediction model is generated based on the association relationship of each key part in the device parts database. For example, the association relationship of the key parts can be "the increase in sales of device model X will lead to an increase in replacement demand for part Y".

[0084] Furthermore, in order to generate an accessory demand linkage prediction model based on the association relationship between each key accessory in the equipment accessory database, it is necessary to first determine the association relationship between the key accessories. Specifically, first call the preset association rule algorithm to perform an association relationship analysis operation on the accessory data in the equipment accessory database; then determine the association relationship corresponding to each accessory data according to the association relationship analysis operation, and the association relationship includes the association relationship between the product and the accessory, and the association relationship between the accessory; and then call the preset confidence threshold corresponding to the accessory data according to different accessory data, and determine the association relationship of the key accessories in the association relationship corresponding to each accessory data. It should be noted that the preset association rule algorithm here performs association analysis through indicators such as frequent item sets and confidence, and analyzes the association relationship between the accessory data in the equipment accessory database. The association relationship corresponding to each accessory data describes the correlation and dependency between different accessories. Optionally, the preset association rule algorithm can be an Apriori algorithm or an FP-growth algorithm.

[0085] By calling the preset association rule algorithm to perform the association analysis operation, the association between products and accessories, that is, which accessories are needed for different products, and the association between these accessories and equipment, can better predict the changes in the demand for accessories when the demand for a certain equipment increases or decreases. By determining the correlation and dependency between accessories, it is possible to better predict the changes in the demand for other accessories when the demand for a certain accessory increases or decreases. The influence between different accessories and the influence between equipment and accessories are comprehensively considered, thereby improving the accuracy of accessory demand prediction. By calling the preset confidence threshold corresponding to the accessory data, according to the accessory association relationship obtained by the association analysis, it can be determined which accessories are key accessories, that is, accessories that have a greater impact on the demand for other accessories. The purpose of this is to further improve the accuracy of the accessory demand linkage prediction model when making predictions based on the mutual influence and dependency between accessories and the association between accessories and equipment.

[0086] Furthermore, when the device accessory database is updated, an update action of the association relationship of key accessories is executed. It can be understood that the update action of the association relationship of key accessories here refers to the process of executing and calling a preset association rule algorithm, performing an association relationship analysis operation on the accessory data in the updated device accessory database; then determining the association relationship corresponding to each accessory data according to the association relationship analysis operation; and then calling the preset confidence threshold corresponding to the accessory data according to different accessory data, and determining the association relationship of key accessories in the association relationship corresponding to each accessory data.

[0087] When new parts appear or the relationship between old parts changes, the equipment parts database will be updated. If the relationship between key parts is not updated in time, outdated or inaccurate data will be used when predicting the demand for parts, resulting in inaccurate prediction results. By performing the update action of the relationship between key parts to reflect the latest correlation and dependency between parts, as well as the relationship between parts and equipment, the parts demand prediction model can better predict the actual demand for parts, thereby ensuring the accuracy and real-time nature of the prediction results of the parts demand prediction model.

[0088] Step S300: After entering the accessories prediction process, the received region identifier and device identifier are input into the accessories demand linkage prediction model, and a short-term accessories prediction result corresponding to the region identifier and device identifier is generated by the accessories demand linkage prediction model.

[0089] Optionally, after entering the accessory prediction process, the region selection interface and the device selection interface are output, and then the region identification and device identification selected by the user are received based on the region selection interface and the device selection interface. It can be understood that the region selection interface here provides region options for the user to select, and the device selection interface provides device options for the user to select. The purpose of this is to avoid the situation where the received region identification and device identification have no corresponding region or no corresponding device.

[0090] As an optional implementation, after the accessories demand linkage prediction model receives the region identifier and the equipment identifier, based on the region identifier and the equipment identifier, the model reads the historical sales data, warranty repair data and accessories consumption data of the target equipment in the target region in the equipment accessories database; then, based on the historical sales data, warranty repair data and accessories consumption data, the model predicts the demand for accessories of the target equipment to obtain the demand for each accessory of the target equipment; and further outputs the demand for each accessory of the target equipment as the short-term accessories prediction result.

[0091] The accessory demand linkage prediction model is based on the historical sales data, warranty maintenance data and accessory consumption data of the target equipment in the target area. It can obtain the demand trend and change law of each accessory of the target equipment in the target area, and then more accurately predict the demand for the accessory in the short term. It can be understood that the short-term accessory prediction results here can be used for the management of accessory inventory to avoid too many accessories occupying resources in the inventory, improve the utilization rate of accessories and funds, and at the same time ensure sufficient supply of accessories to meet the needs of after-sales service. The purpose of this is to enable the accessory management system to predict and determine the number of accessories required according to the demand conditions of the region and equipment, so as to carry out reasonable inventory control.

[0092] In the technical solution provided by this embodiment, by receiving multi-source heterogeneous data transmitted by the accessory data interface, data related to the after-sales service of the equipment from various sources can be obtained. In this way, the accessory information from different channels can be comprehensively collected and integrated; by integrating multi-source heterogeneous data and generating an equipment accessory database with the association relationship of each key accessory, a data basis is provided for realizing the precision of the accessory demand forecast. By generating an accessory demand linkage prediction model based on the association relationship of each key accessory in the equipment accessory database, the mutual influence and demand association between accessories and between equipment and accessories can be more accurately predicted, thereby realizing the dynamic forecast of accessory demand. By inputting the region identifier and the equipment identifier into the accessory demand linkage prediction model, the prediction model can make timely accessory demand forecasts based on the latest data and association relationships, and generate short-term accessory forecast results corresponding to the region and equipment, thereby improving the accuracy of accessory reserves and the timeliness of supply. Therefore, the control method of this accessory management system realizes the precision and dynamic forecast of accessory demand through the use of multi-source heterogeneous data, the association relationship modeling of key accessories and the generation of short-term accessory forecast results, thereby improving the accuracy of accessory reserves and the timeliness of accessory supply.

[0093] Reference Figure 3 Based on the above embodiment, in the second embodiment, after the step of generating the short-term spare parts prediction result corresponding to the region identifier and the equipment identifier by the spare parts demand linkage prediction model, the method further includes:

[0094] Step S410: inputting the short-term spare parts forecast result into the spare parts demand linkage forecast model;

[0095] Step S420: After the accessory demand linkage prediction model receives the short-term accessory prediction result, it outputs a mid- to long-term accessory prediction result based on the short-term accessory prediction result.

[0096] In this embodiment, a deep learning prediction framework supporting multi-source heterogeneous data input is first constructed based on a neural network. The deep learning prediction framework includes two layers. First, a short-term prediction is performed based on historical sales data, warranty maintenance data, and spare parts consumption data to obtain a short-term spare parts prediction result; then the short-term spare parts prediction result is passed to the spare parts demand linkage prediction model to obtain a mid- and long-term spare parts prediction result.

[0097] As an optional implementation method, the configuration list and assembly relationship of each type of equipment are systematically modeled, and a structure tree and graph database corresponding to the equipment are constructed. Then, based on the structure tree corresponding to the equipment, the importance of each accessory, the equipment to which it belongs, and other characteristics are calculated, and the importance of each accessory, the equipment to which it belongs, and other characteristics are integrated into the original data set of the association analysis. Then, based on the preset association rule algorithm, according to the structure tree corresponding to the equipment, the weight associated with the key accessories is increased to obtain the association relationship of the key accessories.

[0098] When a new device is available, the multi-source heterogeneous data of the new device is received based on the accessory data interface, and the device accessory database is updated. Then, based on the updated device accessory database, the association relationship of the key accessories is updated to obtain the updated association relationship of the key accessories. Then, based on the updated association relationship of the key accessories, the accessory demand of the new device is predicted to obtain the preliminary accessory prediction result to avoid cold start failure.

[0099] By continuously updating the association relationship of key accessories, the combination of new and old equipment is closer, further improving the accuracy of the prediction results. By introducing the structure tree corresponding to the equipment, the problem of cold start of the association rule algorithm can be overcome, and the accuracy of the prediction results of the demand for new product accessories can be improved.

[0100] In the technical solution provided in this embodiment, the mid- to long-term parts forecast results are output based on the short-term parts forecast results in order to use the model to make a longer-term parts demand forecast. Short-term parts forecasts are mainly used to meet recent parts demand, while mid- to long-term demand forecasts can be used to formulate supply chain strategies and reserve parts. Through the mid- to long-term parts forecast results, the demand for parts within a longer time frame in the future can be predicted, and production and inventory can be better arranged to meet future parts demand. The parts reserve and supply process of the parts management system is further optimized to improve the accuracy, timeliness and efficiency of parts supply.

[0101] Reference Figure 4 Based on the above embodiment, in the third embodiment, after the step of generating the accessory demand linkage prediction model according to the association relationship of each key accessory in the equipment accessory database, it also includes:

[0102] Step S510: outputting historical forecast data within a preset time period based on the model verification interface at intervals of a preset time period;

[0103] Step S520: adjusting the model parameters of the parts demand linkage prediction model according to the model adjustment parameters received by the model verification interface.

[0104] In this embodiment, after the accessories demand linkage prediction model is generated, the historical prediction data within the preset time period is output at every preset time period. The historical prediction data here is used for the accessories prediction expert group to evaluate the prediction results of the accessories demand linkage prediction model, analyze the accuracy and error of the historical prediction data, and detect the prediction deviations of the accessories demand linkage prediction model in different time periods and regions, so as to determine the defects of the accessories demand linkage prediction model, and then determine the model adjustment parameters. The accessories prediction expert group can input the model adjustment parameters into the model adjustment tool through the model verification interface to adjust the model parameters of the accessories demand linkage prediction model, such as adjusting the weights or influencing factors. It can be understood that the weight here can be the weight associated with the equipment and the key accessories; the influencing factors here can be the historical sales data, warranty maintenance data and accessories consumption data of the target equipment in the target area.

[0105] In the technical solution provided in this embodiment, by outputting historical prediction data and receiving model adjustment parameters, the model parameters of the parts demand linkage prediction model are adjusted according to the model adjustment parameters received by the model verification interface, thereby optimizing the parts demand linkage prediction model and further improving the accuracy of the parts demand linkage prediction model.

[0106] In addition, it can be understood by a person skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the control device of the accessory management system to implement the process steps of the embodiment of the above method.

[0107] Therefore, the present invention also provides a computer-readable storage medium, which stores a control program of the accessories management system. When the control program of the accessories management system is executed by a processor, the various steps of the control method of the accessories management system described in the above embodiment are implemented.

[0108] The computer-readable storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which may store program codes.

[0109] It should be noted that since the storage medium provided in the embodiment of the present application is the storage medium used to implement the method of the embodiment of the present application, based on the method introduced in the embodiment of the present application, the person skilled in the art can understand the specific structure and deformation of the storage medium, so it is not repeated here. All storage media used in the method of the embodiment of the present application belong to the scope of protection of this application.

[0110] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0111] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0112] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0114] It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claim. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention may be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second and third etc. does not indicate any order. These words may be interpreted as names.

[0115] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0116] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A control method for a spare parts management system, characterized in that: The control method of the accessories management system comprises the following steps: After receiving the multi-source heterogeneous data, attach data identifiers to the multi-source heterogeneous data according to the sources of the multi-source heterogeneous data to generate accessory data, wherein the data identifiers include product attribute identifiers, accessory relationship identifiers, and maintenance time identifiers, and the multi-source heterogeneous data include equipment usage information, historical spare parts storage and consumption data, warranty maintenance records, and equipment detection data; Generate a device accessories database based on the data identifiers of each of the accessory data; Calling a preset association rule algorithm to perform an association relationship analysis operation on the accessory data in the device accessory database, wherein the preset association rule algorithm performs association analysis through frequent item sets and confidence indicators to analyze the association relationship between the accessory data in the device accessory database, wherein the association relationship describes the correlation and dependency between different accessories; According to the association analysis operation, determining the association corresponding to each of the accessory data, the association including the association between the product and the accessory, and the association between the accessory and the accessory; According to different accessory data, calling the preset confidence threshold corresponding to the accessory data, and determining the association relationship of the key accessories in the association relationship corresponding to each accessory data; Generate a linkage prediction model for accessory demand based on the association relationship between key accessories in the equipment accessories database; After entering the spare parts prediction process, the received region identifier and device identifier are input into the spare parts demand linkage prediction model, and a short-term spare parts prediction result corresponding to the region identifier and device identifier is generated by the spare parts demand linkage prediction model; The step of inputting the received region identifier and device identifier into the parts demand linkage prediction model, and generating a short-term parts prediction result corresponding to the region identifier and device identifier by using the parts demand linkage prediction model comprises: After receiving the region identifier and the device identifier, the parts demand linkage prediction model reads historical sales data, warranty repair data, and parts consumption data of the target device in the target region from the device parts database based on the region identifier and the device identifier; According to the historical sales data, warranty repair data and spare parts consumption data, demand forecasting is performed on the spare parts of the target device to obtain the demand quantity of each spare part of the target device; The demand quantity of each accessory of the target device is output as the short-term accessory prediction result.

2. The control method of the spare parts management system according to claim 1, characterized in that: After the step of generating the short-term spare parts prediction result corresponding to the region identifier and the equipment identifier by the spare parts demand linkage prediction model, the method further includes: Inputting the short-term spare parts forecast result into the spare parts demand linkage forecast model; After the accessory demand linkage prediction model receives the short-term accessory prediction result, it outputs a mid- to long-term accessory prediction result based on the short-term accessory prediction result.

3. The control method of the spare parts management system according to claim 1, characterized in that: After receiving the multi-source heterogeneous data, before the step of attaching a data identifier to the multi-source heterogeneous data according to the source of the multi-source heterogeneous data and generating the accessory data, the step further includes: Establish a connection between the parts data interface and the parts cloud warehouse platform, enterprise resource system and customer management system; When the data related to the after-sales service of the equipment in the accessories cloud warehouse platform, the enterprise resource system and the customer management system are updated, the data related to the after-sales service of the equipment are received based on the accessories data interface.

4. The control method of the spare parts management system according to claim 1, characterized in that: After the step of calling the preset confidence threshold corresponding to the accessory data according to different accessory data and determining the association relationship of the key accessories in the association relationship corresponding to each accessory data, the method further includes: When the device accessory database is updated, an update action of the association relationship of key accessories is executed.

5. The control method of the spare parts management system according to claim 1, characterized in that: After the step of generating the accessory demand linkage prediction model according to the association relationship of each key accessory in the equipment accessory database, the method further includes: At every preset time period, the historical forecast data within the preset time period is output based on the model verification interface; According to the model adjustment parameters received by the model verification interface, the model parameters of the accessory demand linkage prediction model are adjusted.

6. A control device for a spare parts management system, characterized in that: The control device of the accessory management system includes: a memory, a processor, and a control program of the accessory management system stored in the memory and executable on the processor, wherein the control program of the accessory management system is configured to implement the steps of the control method of the accessory management system as described in any one of claims 1 to 5.

7. A readable storage medium, characterized in that: The readable storage medium stores a control program of the accessory management system, and when the control program of the accessory management system is executed by the processor, the steps of the control method of the accessory management system according to any one of claims 1 to 5 are implemented.

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